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The hidden cost of ambiguous energy software terminology

Sneha Vasudevan is a project management lead at Uplight. In the face of rapid load growth, the electricity sector is experiencing unprecedented investment in advanced technologies as organizations try to balance reliability, affordability and decarbonization. Transformation is happening on both sides of the grid, with the scale of consumer adoption of distributed energy resources approaching that […]

Sneha Vasudevan is a project management lead at Uplight.

In the face of rapid load growth, the electricity sector is experiencing unprecedented investment in advanced technologies as organizations try to balance reliability, affordability and decarbonization. Transformation is happening on both sides of the grid, with the scale of consumer adoption of distributed energy resources approaching that of utility-scale generation capacity. Residential customers are installing heat pumps, electric vehicles and charging equipment, solar panels, and home batteries while food corporations, logistics companies and school districts electrify their vehicle fleets and implement sophisticated energy management systems. 

The consumer distributed energy resource hardware investment boom is resulting in increased utility spending on sophisticated software platforms to manage thousands of independently owned energy assets. Unlike the hardware world — where there is broad agreement on technical specifications of a solar panel or EV or battery — software solutions lack definitional clarity. Terms like “virtual power plant,” “fleet energy management system,” and “distributed energy resource management system” mean different things to different vendors and utilities.

Successfully adapting to load growth and DER adoption hinges on the successful, scalable deployment of these software solutions. This depends on clear, mutual understanding of requirements, capabilities and outcomes among all parties. Despite the best intentions of utilities and vendors, without definitional clarity across energy software solutions, the industry remains stuck in endless scope changes and cost overruns instead of building the grid of the future.

Where the industry gets lost in translation

The lack of industry-wide consensus on standardized definitions for software technologies, capabilities and associated service offerings represents more than a communications issue — it’s a major barrier to meeting the increased load demand.

Without shared definitions, the industry duplicates effort, misses synergies and stalls the transition to smarter energy systems. For utilities, this creates operational blind spots where meaningful KPIs become difficult to establish and procurement requirements remain vague, undermining system reliability and measurement. For vendors, the lack of clear definitions turns roadmap planning into guesswork — they’re building solutions for undefined problems while struggling to stand out. At the project level, teams waste time on redundant work and misaligned deliverables and at the industry level, innovation stagnates as collective progress fragments into isolated efforts. 

The result mirrors a dysfunctional restaurant ecosystem: utilities act like confused customers who can’t articulate what they want to order, while vendors operate like chefs who don’t know which dishes to cook — leaving both sides frustrated and the entire energy transition undernourished.

Three key areas lacking definitional clarity continue to undermine industry progress. First, technology categorization suffers from unclear scope — stakeholders can’t agree on what fundamental characteristics define emerging energy technologies, leaving everyone with different baseline assumptions. Second, technical capabilities remain inconsistently defined — vendors describe the same core platform features differently, creating confusion about actual functionality and requirements. Third, service boundaries blur between overlapping platforms, making it challenging to determine system scope during procurement and project planning.

Colorado’s example illustrates the problem: utility commissioners couldn’t agree whether Xcel Energy’s customer solar and battery program constituted a “virtual power plant” or “prosumer tariffs.” The same technology gets classified differently across states — rooftop solar with battery storage might be legally defined as part of a “virtual power plant” in Colorado and “distributed energy aggregation” in California.

These regulatory inconsistencies complicate compliance across jurisdictions, delay program deployment and create dramatically different outcomes for customers. Technology companies struggle to build scalable businesses when the same software must navigate entirely different regulatory frameworks in each market. These definitional disconnects become costly during project implementation, forcing expensive remediation: contract renegotiations, additional vendor contracts, system integrations and project delays.

Building the foundation for efficient energy technology deployment

Solving this challenge requires the energy industry to coordinate action across four critical areas:

Develop a Common Technology Taxonomy: Eliminate guesswork by creating a classification system for energy management technologies and what they actually do — monitoring, analytics, optimization, control — with standardized definitions that industry associations and regulatory bodies can agree on. Organizations including SEPA, GridWise Alliance, Total Grid Orchestration Alliance, VP3, The Flex Coalition, PLMA and Advanced Energy United are advancing various aspects of these taxonomies, though comprehensive standards remain years away. 

Define Platform-Specific Service Models: Establish clear, standardized descriptions for each technology category that spell out exactly what capabilities and services each platform type delivers. Buyers can reduce procurement risks by changing their approach from technology-category shopping to capability and use case-specific sourcing. Instead of requesting a “virtual power plant solution,” successful RFPs should detail specific pain points: “We need bidirectional energy trading with 15-minute dispatch intervals, integrated with our existing SCADA system, capable of managing 500+ residential solar installations while providing frequency regulation services to our regional grid operator.” This granular approach forces vendors to respond with actual capabilities rather than marketing buzzwords.

Align Regulatory Frameworks with Technical Reality: Encourage policymakers to use standardized definitions that reflect what these technologies actually can and can’t do, reducing compliance complexity and enabling consistent performance benchmarking across jurisdictions. Progress in supporting FERC Order 2222 implementation demonstrates movement in this direction, though regulatory inconsistencies across jurisdictions remain a challenge.

Enable Interoperability Through Open Standards: Develop technical standards built on clear service definitions that allow platforms to actually integrate with each other, eliminating the costly custom development and system integration challenges that currently plague energy technology deployments. IEEE’s P2030.13 Working Group is developing guides for fast-charging station management, while IEC 61850 standards for smart grid communications are being mapped to aggregated DER/VPP applications. EPRI leads major VPP-focused initiatives — the Mercury Initiative for standard data models between aggregators and OEMs, and the FlexIT Initiative for utility-aggregator integrations. 

Language shapes market reality. Without shared understanding and unified standards, even the most innovative energy technologies risk remaining isolated experiments that drain budgets rather than deliver value. The path forward requires moving from costly pilot projects to scalable solutions, from scattered procurement failures to systematic transformation.

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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